CN110231452A - The method for predicting soil clay particle content or salt content based on adsorption isothermal curve - Google Patents
The method for predicting soil clay particle content or salt content based on adsorption isothermal curve Download PDFInfo
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Abstract
The present invention relates to a kind of methods for predicting soil clay particle content or salt content based on adsorption isothermal curve, comprising: the selected part soil sample in large sample measures the clay content and salt content of the pedotheque of selection；The vapor adsorption isothermal curve eyeball for the soil sample chosen is measured, then is fitted to obtain adsorption curve and desorption curve；Calculate the geometrical characteristic area of the soil water vapour adsorption isothermal curve for the soil sample chosen；Significant correlation analysis is carried out to the geometrical characteristic area of soil salt content, clay content and adsorption isothermal curve, determines that geometrical characteristic area is influenced significant sensitivity interval by soil clay particle content and salt content；It establishes soil clay particle content, the prediction model formula in salt content and sensitivity interval between geometric area, and determines the scope of application；Remaining a large amount of soil sample is obtained into clay content or salt content by prediction model formula.The present invention can efficiently and economically predict soil clay particle content or salt content.
Description
Technical field
The present invention relates to the technical fields of soil physical properties prediction, and in particular to one kind is inhaled based on soil water vapour isothermal
The method of a large amount of soil sample clay contents of attached curve prediction or salt content.
Background technique
The soil texture, especially clay content control the size and speed of a variety of physics in soil, chemistry and hydrologic process
Rate, this largely determines the moisture holding capacity, hydraulic characteristic(s) and cation exchange capacity (CEC) of soil.In addition, raw to soil
State system, resistance of soil and permeability have a significant impact, and related with biological activity of soil, Soil Respiration.
The conventional method of experimental determination soil clay particle content generallys use wet screening or dry screen combination densimeter or pipette
Method, when sample size is larger, measurement is got up time-consuming and laborious.More advanced measurement method includes: x-ray attenuation method, laser
Diffraction approach and spectroscopic methodology, the instrument of these methods costly, may need to calibrate before use.Therefore, design it is simple, quickly,
Economic clay particle content method is extremely important.Non- salt marsh is predicted based on soil water vapour adsorption isothermal curve currently, having
The model of native clay content.Wherein, soil vapor adsorption isothermal curve describes between relative humidity (RH) and hygroscopic moisture
Relationship, be the dry end of characteristic curve of soil moisture, it can predict a variety of soil physical properties, such as soil specific surface area, viscous
Grain and collapses ability etc. at content, the condition that is measured suitable for large sample quantity and large scale (quoted from Arthur E, Tuller M,
Moldrup P,et al.Evaluation of theoretical and empirical water vapor sorption
isotherm models for soils[J].Water Resources Research,2016,52(1):190-205.).?
Clay prediction model is the relationship for establishing hygroscopic moisture and clay content, for example, Wuddivira et al. proposed one in 2012
Regression model (WM), the hygroscopic moisture content prediction clay content that relative humidity is 50%；Chen Chong et al. developed CM in 2014
Model, for estimating the clay content under any given relative humidity；Arthur et al. improves CM model, has obtained MCM mould
Type, and the regression model (ASM and LOM) of hysteresis and the content of organic matter is considered at exploitation in 2015 one, while proposing to estimate
Calculate three main sources of error: the property of high organic carbon content, mud content and clay mineral (quoted from Arthur E,
Tuller M,Moldrup P,et al.Prediction of clay content from water vapour
sorption isotherms considering hysteresis and soil organic matter content[J]
.European Journal of Soil Science,2015,66(1):206-217.).However, these models are non-salt
It is established in stain soil, in the soil influenced by salinity, the relationship between clay content and soil hygroscopic water becomes more multiple
Miscellaneous, the applicability in salt-affected soil is unknown.
Salinity directly affects a variety of soil physical chemistry processes, is one of the principal element for restricting plant growth.However, traditional
Soil salt measuring method process is cumbersome, same time-consuming and laborious, cannot quickly judge and measure soil salinization level.Wherein,
Electrical conductivity method can directly measure electrical conductivity of soil solution, be not required to correct, and have higher precision, but Soil moisture sampler and
Salt sub-sensor is more demanding to soil moisture, and the response time is long, is not suitable for the short-term or violent of dry environment and soil salt
Migration process monitoring.And electric-resistivity method and electromagnetic induction method can be realized the Quick Acquisition of soil salt data on maturity in field, when
Domain bounce technique can measure soil moisture and salinity simultaneously, but simultaneously by soil moisture content, quality, salt content, organic matter and temperature
The influence of equal many factors, therefore the process of extraction soil salt data is also sufficiently complex from Instrumental results, and essence
Spend limited (quoted from Liu Meixian, in-situ determination method [J] soil of Yang Jingsong soil salt, 2011,43 (5)).Currently, high
Spectrum be carry out the soil salinization monitor ideal monitoring means, by multi-source, multidate high-spectral data to salt marsh
Change more serious region with good monitoring effect, but also spectral reflectance is influenced by many factors.Although existing
Scholar makes a large amount of improvement to EO-1 hyperion inverting salt sub-model, but effect does not reach salinity inversion accuracy still, and big
There are also many difficulty (Zhang T T, Zeng S L, Gao Y, et al.Using hyperspectral for monitoring on scale
vegetation indices as a proxy to monitor soil salinity[J].Ecological
Indicators,2011,11(6):0-1562.)。
Therefore, it is necessary to determine that a kind of prediction clay content and salt content have efficacious prescriptions for large sample quantity soil
Method.
Summary of the invention
Soil clay particle content and salt content are predicted based on adsorption isothermal curve the purpose of the present invention is to provide a kind of
Method, this method can more economically, efficiently predict soil clay particle content, salt content when test sample quantity is larger,
Save time and human resources.
The present invention solves scheme used by above-mentioned technical problem:
A method of soil clay particle content or salt content are predicted based on adsorption isothermal curve, which is characterized in that including
Following steps:
S1: the selected part soil sample in large sample measures the viscous of the pedotheque of selection with traditional measurement method
Grain content and salt content；
S2: the vapor adsorption isothermal curve eyeball of the soil sample of selection is measured, then is obtained completely by fitting
Adsorption curve and desorption curve；
S3: calculating the geometrical characteristic area of the soil water vapour adsorption isothermal curve of the soil sample of selection, including moisture absorption,
The geometrical characteristic area of desorption and lag；
S4: special using geometry of the Multiple Regression Analysis Method to soil salt content, clay content and adsorption isothermal curve
It levies area and carries out significant correlation analysis, it is significant quick to determine that geometrical characteristic area is influenced by soil clay particle content and salt content
Between sensillary area；
S5: establishing soil clay particle content, the prediction model formula in salt content and sensitivity interval between geometric area, and
Determine the scope of application；
S6: the soil sample for needing to measure clay content or salt content is passed through into above-mentioned corresponding prediction model formula meter
Calculation obtains its clay content or salt content.
Further, soil of the soil sample of selection from 4 kinds of salinity levels is soil saturation leaching liquor conductance respectively
Slight salinized soil that non-salty-soil that rate is 0~4.5dS/m, soil saturation leaching liquor conductivity are 4.5~9dS/m, soil are full
With the severe of moderate salinized soil and soil saturation leaching liquor conductivity greater than 18dS/m that leaching liquor conductivity is 9~18dS/m
Salinized soil, wherein the soil sample of every kind of salinity level all have it is multiple.
Further, in step s 4 to the geometrical characteristic face of soil salt content, clay content and adsorption isothermal curve
When product carries out significant correlation analysis, the geometrical characteristic area for choosing the moisture absorption under a variety of RH step-lengths, desorption and lag is counted
It calculates and analyzes.
Further, the step-length of RH is chosen for 0.02,0.1 and 0.9 respectively.
Further, step S4 further include:
Significant correlation analysis is carried out in the geometrical characteristic area to soil salt, clay content and adsorption isothermal curve
When, establish the mathematic(al) representation of triadic relation:
A=aEc+bCL+c
Wherein, ECFor conductivity, dS/m；CL is clay content, %；A be soil water vapour adsorption isothermal curve moisture absorption,
The geometrical characteristic area of desorption and lag, the sub- area including the gross area and a variety of RH step-lengths section；A, b and c is regression coefficient；
Step will be fitted in the clay content of the soil sample of all selections, salt content obtained in step S1 and step S3
The adsorption curve and desorption curve that the vapor adsorption isothermal curve eyeball of soil sample obtains in rapid S2 are corresponding a variety of
Moisture absorption area, desorption area and the lag area that RH step-length interval computation obtains are substituted into respectively in above-mentioned mathematic(al) representation, respectively
Calculate the numerical value, fitting area and eyeball area of coefficient a, b, c of all soil samples chosen in each RH step-length section
Between coefficient of determination R2, and test to the conspicuousness of coefficient a, b；
According to the coefficient of determination R between the significance test result of coefficient a, b, matched curve area and eyeball area2
It is quick that value determines that the geometrical characteristic area of the soil sample of all selections is significantly affected by soil clay particle content or salt content
Between sensillary area, i.e., the significant correlation of coefficient a, the b of the soil sample of more all selections in a variety of RH step-lengths section and certainly
Determine coefficients R2Value, wherein a is that significant related coefficient is then influenced significantly by salt content, and b is that significant related coefficient is then contained by clay
Amount influences significantly, and the coefficient of determination R between matched curve area and eyeball area2The maximum corresponding section of value is all
The sensitivity interval of the soil sample of selection.
Further, the prediction model formula for being suitable for all soil, prediction model formula are established are as follows:
The sensitivity interval of all soil is RH=0.83~0.85, the prediction model formula of salt content are as follows: Ec(p)=-
4.66×107SAa 2+1.38×105SAa- 44.18, wherein Ec(p)For the conductivity of soil prediction, SAaFor the moisture absorption of sensitivity interval
Area under the curve.
Further, step S4 further include:
Significant correlation analysis is carried out in the geometrical characteristic area to soil salt, clay content and adsorption isothermal curve
When, establish the mathematic(al) representation of triadic relation:
A=aEc+bCL+c
Wherein, ECFor conductivity, dS/m；CL is clay content, %；A be soil water vapour adsorption isothermal curve moisture absorption,
The geometrical characteristic area of desorption and lag, the sub- area including the gross area and a variety of RH step-lengths section；A, b and c is regression coefficient；
Step will be fitted in the clay content of the soil sample of all selections, salt content obtained in step S1 and step S3
The adsorption curve and desorption curve that the vapor adsorption isothermal curve eyeball of soil sample obtains in rapid S2 are corresponding a variety of
Moisture absorption area, desorption area and the lag area that RH step-length interval computation obtains are substituted into respectively in above-mentioned mathematic(al) representation, respectively
Calculate the numerical value, fitting area and eyeball area of coefficient a, b, c of all soil samples chosen in each RH step-length section
Between coefficient of determination R2, and test to the conspicuousness of coefficient a, b；According to the significance test result of coefficient a, b, intend
Close the coefficient of determination R between area under the curve and eyeball area2Value determines the geometrical characteristic face of the soil sample of all selections
The sensitivity interval that product is significantly affected by soil clay particle content or salt content, i.e., the soil sample of more all selections is in a variety of RH
The significant correlation and coefficient of determination R of coefficient a, b in step-length section2Value, wherein a be significant related coefficient then its by salt
Point content influence it is significant, b be significant related coefficient then its influenced significantly by clay content, and be fitted area and eyeball area it
Between coefficient of determination R2The maximum corresponding section of value is the sensitivity interval of the soil sample of all selections；
Further according to the classification for four kinds of salinity levels that the above-mentioned soil sample to selection carries out, every kind of salinity is individually calculated
Horizontal soil sample is in the numerical value of coefficient a, b, c in each RH step-length section, the significant correlation of coefficient a, b and fitting area
Coefficient of determination R between eyeball area2Value；According to the significant correlation of coefficient a, b, fitting area and eyeball area it
Between coefficient of determination R2Determine the geometrical characteristic area of the soil sample of four kinds of salinity levels by soil clay particle content or salinity
The sensitivity interval that content significantly affects, wherein for the soil sample of every kind of salinity level, a be significant related coefficient then its by
Salt content influence it is significant, b be significant related coefficient then its influenced significantly by clay content, and be fitted area and eyeball area
Between coefficient of determination R2The maximum corresponding section of value is its sensitivity interval.
Further, the soil for establishing all soil and four kinds of salinity levels uses geometric surface in its sensibility section respectively
The model formation of product prediction soil clay particle content or salt content, prediction model formula are respectively as follows:
The sensitivity interval of all soil is RH=0.83~0.85, the prediction model formula of salt content are as follows: Ec(p)=-
4.66×107SAa 2+1.38×105SAa- 44.18, wherein Ec(p)Conductivity, SA are predicted for soilaIt is bent for the moisture absorption of sensitivity interval
Line area；
The sensitivity interval of non-salty-soil is RH=0.85~0.87, clay content prediction model formula are as follows: CLp=4.34 ×
105SAd- 188.83, wherein for CLpTo predict clay content, SAdFor the desorption curve area of sensitivity interval；
The sensitivity interval of slight salinized soil is RH=0.81~0.83, clay content prediction model formula are as follows: CLp=-
3.09×105SAh+ 43.97, wherein for CLpTo predict clay content, SAhFor the lag area of sensitivity interval；
The sensitivity interval of moderate salinized soil is RH=0.87~0.89, salt content prediction model formula are as follows: Ec(p)=
9.38×104SAd- 41.82, wherein Ec(p)To predict conductivity content, SAdFor the desorption curve area of sensitivity interval；
The sensitivity interval of severe salinized soil is RH=0.91~0.93, salt content prediction model formula are as follows: Ec(p)=
2.21×104SAa+ 4.32, wherein Ec(p)To predict conductivity content, SAaFor the sucting wet curve area of sensitivity interval.
Further, when predicting great amount of samples, first each soil sample in a large amount of soil samples is passed through
The above-mentioned substantially salinity that each soil sample is calculated suitable for the salt content prediction model formula of all soil samples
Content judges it particularly belongs to which kind of salt in four kinds of salinity levels further according to the substantially salt content of each soil sample prediction
Point horizontal soil, finally according to the prediction model formula that its corresponding salinity level is applicable in carry out prediction soil clay particle content or
Salt content.
Compared with prior art, the present invention at least has the advantages that the present invention is based on salt contents to soil
Adsorption isothermal curve tendency significantly affects, and fully considers this factor of salt content to the area shadow of adsorption isothermal curve
It rings, and soil sample is classified according to salt content, to analyze clay content, salt content and adsorption isothermal curve
Correlation between geometrical characteristic area determines that adsorption isothermal curve geometrical characteristic area is contained by soil clay particle content and salinity
Amount influences significant sensitivity interval, establishes the salt content prediction model formula for being suitable for all soil and is respectively suitable for four kinds
Optimum prediction model formation in the soil clay particle content or salt content of salinity level and sensitivity interval between geometric area, should
Prediction model formula predictions data are accurate, can be first according to suitable for all soil samples when test sample quantity is larger
Salinity prediction model formula carry out calculating prediction soil sample substantially salt content, further according to prediction soil sample it is big
It causes salt content to judge that it is specifically in which level of four kinds of salinity levels, is finally applicable according to its corresponding salinity level
Prediction model formula carry out prediction soil clay particle content or salt content, this method high-efficiency and economic and prediction data is more accurate,
Save a large amount of time of measuring and human resources.
Detailed description of the invention
Fig. 1 is salt content to soil adsorption isothermal curve tendency influence diagram；
Fig. 2 is the soil sample quality distribution map that the embodiment of the present invention is chosen；
Fig. 3 is to be fitted to obtain with thermoisopleth absorption measurement of the DLP model to the soil sample that the embodiment of the present invention is chosen
Sucting wet curve and desorption curve；
Fig. 4 is existing model evaluation figure, and relatively more selected model (WM, MCM and LOM model) predicts that clay contains in salinized soil
The precision of amount, such as MCMa0.5Middle a is sucting wet curve, and 0.5 is RH=50%；
Fig. 5 is sucting wet curve/desorption curve geometrical characteristic area, θ0.03Moisture absorption water content rate or solution when for RH=0.03
Inhale moisture content, TAa/TAdIt is the moisture absorption and the desorption gross area that curve is surrounded with X-axis, Y-axis, SAa/SAdIt is corresponding RH step-length area
Interior moisture absorption and the sub- area of desorption；
Fig. 6 is the geometrical characteristic area of Delay Process, θa0.03Moisture absorption water content rate when for RH=0.03, TAhIt is moisture absorption song
The lag gross area that line and desorption curve surround, SAhIt is the sub- area of lag in corresponding RH step-length section.
Specific embodiment
For a better understanding of the present invention, the following examples are to further explanation of the invention, but the contents of the present invention
It is not limited solely to the following examples.
The water content in non-salty-soil only considered mostly to the model of the clay content prediction in soil in the prior art
Estimate calculating with the parameters such as matric potential, table 1 lists several existing clay prediction models, see the table below 1.
Table 1 has several clay content prediction models
CL is clay content, % in table 1；θmFor moisture absorption water content, %；ψ is matric potential；A is empirical regression coefficient.
It will be seen that existing several clay prediction models only considered hygroscopic moisture water content and base from table 1
The influence of matter gesture, but soil moisture content is significantly affected in Salts In Salt-affected Soil content, the present inventor tests several salinities and contains
The adsorption isothermal curve for measuring different soil, from Fig. 1 it will be seen that the conductivity in salt content i.e. figure is direct
The tendency of adsorption isothermal curve is influenced, i.e., salt content significantly affects the water content of soil in soil.Therefore, we it can be concluded that
In salinized soil, existing prediction model be it is unworkable, the present inventor below will verify this.
Based on above-mentioned problem, the present invention provides one kind based on adsorption isothermal curve prediction soil clay particle content or salinity
The method of content, includes the following steps:
S1: selecting 35 soil samples at random in large sample soil, and in the present embodiment, the present inventor chooses 35 samples
This measures the clay of each soil sample using traditional measurement method it is of course also possible to choose other quantity for being not less than 20
Content and salt content；Wherein, soil clay particle content is measured using wet screening-pipette method, soil texture distribution is as shown in Figure 2；
Using the soil extraction conductivity of DDSJ-305F type electric conductivity instrument measurement 1:5, then it is converted into saturation extract conductivity
(ECe=7.4 × EC1:5).This patent indicates soil salt size with conductivity.35 soil sample quality distribution situations are as schemed
Shown in 2.
S2: being divided into 4 kinds of salinity grade soils according to salinized soil classification standard for 35 soil samples, in the present embodiment,
Non-salty-soil is 6 samples, and slight salinized soil is 7 samples, and moderate salinized soil is 5 samples, and severe salinized soil is 17 samples
This, the specific criteria for classifying is as shown in table 2 below:
The classification standard (FAO) of 2 salinized soil of table
S3: the vapor adsorption isothermal curve eyeball of 35 soil samples is measured using vapor sorption analyzer, specifically
Method are as follows: pedotheque first dry or that wetting 3.5g is air-dried uses high-precision using the chilled mirror dew point technology automatic measurement flow of water
Magnetic balance measures sample quality；It is instrument setting parameter: relative humidity (RH) range: 0.03-0.93 below；Resolution ratio: 0.02；
Temperature: 25 DEG C, high-precision measurement soil hygroscopic and desorption isotherm eyeball in 24~48h；DLP models fitting is used again
Obtain complete moisture absorption, desorption curve (see Fig. 3), the mathematic(al) representation of DLP model are as follows:
Wherein b0、b1、b2And b3For multinomial coefficient.
Here for, the present inventor pair poor to salt-affected soil prediction effect that verify the present invention existing prediction model above-mentioned
Existing prediction model in table 1 is evaluated to examine its prediction effect.
In the verifying, choosing relative humidity RH is 50% and 90% for example, the regression coefficient of sucting wet curve is
10.6 and 5.79, the regression coefficient of desorption curve is 8.73 and 5.43.
The index of evaluation model performance is the coefficient of determination (R2), opposite root-mean-square error (RRMSE) and opposite average absolute
Error (RMAE):
Wherein, CLiTo survey clay content, CLpTo predict clay content,To survey average clay content, n is sample
This quantity.Fig. 4 shows to existing model evaluation as a result, relatively more selected model (WM, MCM and LOM model) is in salinized soil
Predict the precision of clay content, such as a is sucting wet curve in MCMa0.5,0.5 is RH=50%；As can be seen from Figure 4 have
3 clay prediction models prediction effect it is poor.
S4: establishing the geometrical characteristic area of soil water vapour adsorption isothermal curve, moisture absorption area including a variety of RH step-lengths,
Area and lag area are desorbed, in the present embodiment, the present invention chooses three kinds of RH step-lengths and calculates and assess, wherein three kinds of RH
Step-length is respectively as follows: 0.02,0.1 and 0.9, and as RH=0.9, the geometric area of calculating is the geometrical characteristic gross area, geometrical characteristic
Area is calculated using mathematic integral, and specific geometrical characteristic area schematic diagram is shown in Fig. 5 and Fig. 6.
S5: special using geometry of the Multiple Regression Analysis Method to soil salt content, clay content and adsorption isothermal curve
It levies area and carries out significant correlation analysis, establish the mathematic(al) representation of triadic relation are as follows:
A=aEc+bCL+c (5)
Wherein ECFor conductivity, dS/m；A is the geometrical characteristic area of soil water vapour adsorption isothermal curve, including total face
Long-pending and a variety of RH step-lengths section sub- area；A, b and c is regression coefficient.
The adsorption curve and desorption curve that will be fitted in clay content obtained in step S1, salt content and step S3
Moisture absorption area, parsing area and the lag area in the various RH step-lengths section being calculated substitute into formula respectively, count respectively
All soil samples that calculation is chosen are between the numerical value, fitting area and eyeball area of the coefficient a, b, c in each RH step-length section
Coefficient of determination R2And significance test is carried out to coefficient a, b.Significance test result, fitting face further according to coefficient a, b
Coefficient of determination R between long-pending and eyeball area2Determine that the geometrical characteristic area for all soil samples chosen is viscous by soil
Grain content and/or salt content influence significant sensitivity interval.Specifically, according to coefficient a, b significance test result can be true
Determine geometrical characteristic area to be significantly affected by salt content or significantly affected by clay content, if coefficient a is significant correlation
Coefficient (i.e. P < 0.05) illustrates that geometrical characteristic area is significantly affected by salt content, and vice versa；If coefficient b is significant correlation
Coefficient (i.e. P < 0.05) illustrates that geometrical characteristic area is significantly affected by clay content；And R2Then be judgement fitting desorption curve/
Correlation between sucting wet curve and eyeball, R2Bigger, then the correlation of the two is bigger, this also illustrates that the desorption of fitting is bent
Line/sucting wet curve can more describe eyeball.Therefore, when determining the sensitivity interval of all soil, first determine that coefficient a is significant phase
Relationship number or coefficient b are significant related coefficient, then the coefficient of determination of more all soil samples in a variety of RH step-lengths section
R2Value, the coefficient of determination R being fitted between area and eyeball area2Maximum corresponding section is its sensitivity interval.It is deposited in table 3
It makes decision coefficients R in two kinds of RH step-lengths2Identical situation, due to retaining three decimals, under actual conditions, RH step-length after decimal point
It is smaller, R2Slightly increase.
The classification for carrying out four kinds of salinity levels according to the soil sample to selection later, individually calculates every kind of salinity level
The numerical value of the coefficient a, b, c in each RH step-length section of soil sample, the coefficient of determination being fitted between area and eyeball area
R2And significance test is carried out to coefficient a, b；According to the significance test result of coefficient a, b, fitting area and eyeball face
Coefficient of determination R between product2Determine the geometrical characteristic area of the soil sample of four kinds of salinity levels by soil clay particle content or
The sensitivity interval that salt content significantly affects, the soil sample of the method and above-mentioned all selections that determine sensitivity interval is really
Determine that method is identical, i.e. the soil sample for every kind of salinity level, first determines that coefficient a is significant related coefficient or coefficient b is
Significant related coefficient；Coefficient of determination R of the soil sample of more every kind of salinity level in a variety of RH step-lengths section again2Value is intended
Coefficient of determination R between chalaza area and eyeball area2It is this kind of salinity level soil for the corresponding section of maximum value
Sensitivity interval.
Table 3 shows the soil of all soil samples being calculated and four kinds of different salinity levels in formula (5)
Significance test result, fitting area and the actual measurement of the numerical value and coefficient a, b, c of the sample a in a variety of RH step-lengths section, b, c
Coefficient of determination R between point area2, a, b, c and R2Specific calculated result is shown in Table 3:
Table 3 is clay content (%), salt amount content (dS/m) and Adsorption and desorption and the total face of lag of 35 soil samples
Relationship between product/sub- area
* indicates p~value < 0.01；* p~value < 0.1 is indicated；It is the geometrical characteristic gross area when RH=0.9
(3 are shown in Table) when analyzing 35 soil samples, and coefficient a has notable contribution to regression equation, and soil salt contains
Amount adsorption area, desorption area and lag area between it is closely related, the area include step-length be 0.9 when the gross area and step
Sub- area when a length of 0.1 or 0.02.And with the reduction of RH step-length, sub- area is related between salinity, clay content
Property (R2=0.804~0.928) it is greater than the gross area (R2=0.733~0.914).For the soil sample of all selections, in moisture absorption
In curve, from coefficient a and R2Value as can be seen that in RH=0.83~0.85, coefficient a be conspicuousness related coefficient (P <
, and R 0.01)2Value is maximum, therefore in this section, sucting wet curve is influenced significant and fitting regression curve by soil salt content
Correlation maximum between eyeball, therefore being influenced significant sucting wet curve sensibility section by soil salt content is RH=
0.83~0.85；With same method, we can also obtain from table 3, be influenced significantly to desorb by soil salt content bent
Line sensibility section is RH=0.91~0.93, and being influenced significant hysteresis sensibility section by soil salt content is RH=
0.53~0.55.Compare the R of three kinds of curves again2Value, it can be seen that for all soil samples, in RH=0.83~0.85
Sucting wet curve R2Value is maximum, therefore can determine is influenced significantly the soil sample of all selections by soil salt content
Best sensitivity interval be sucting wet curve RH=0.83~0.85.
35 soil samples are divided into 4 kinds of salinity levels.In the desorption and Delay Process of non-salty-soil, b pairs of coefficient
Regression equation has notable contribution, from table 3 it can be seen that the sub- area of the desorption in RH=0.85~0.87 and RH=0.79~0.81
The sub- area of lag in, coefficient b is significant related coefficient (P < 0.01), and R2Value is maximum on corresponding curve, therefore RH=
0.85~0.87 sub- area of desorption and the sub- area of lag of RH=0.79~0.81 are influenced significant by soil clay particle content.?
In the slight Delay Process of salinized soil and the moisture absorption process of moderate salinized soil, coefficient b equally has notable contribution to equation.For light
Salinized soil is spent, at the sub- area of the lag of RH=0.81~0.83, coefficient b is significant related coefficient (P < 0.01), and the section pair
The R answered2Value is maximum, therefore closely related between the sub- area of the lag of RH=0.81~0.83 and clay content, and can determine the area
Between be its sensitivity interval.With same method we it can be concluded that, moisture absorption for moderate salinized soil, in RH=0.15~0.17
The corresponding R in the closely related and section between sub- area and clay content2Value is maximum, therefore RH=0.15~0.17 is that its moisture absorption is bent
The sensitivity interval of line.With the increase of salinity level, influence of the clay content to geometrical characteristic area reduces, major influence factors
Become salt content (can from table 4 coefficient a, b significance test result find out).Therefore, under normal circumstances, in non-salty-soil
In light salinized soil, clay content has a significant impact the geometrical characteristic area of soil water vapour adsorption isothermal curve, certain area
Interior geometrical characteristic area has prediction potential to soil clay particle content.
For moderate salinized soil, only there are significant correlations between desorption area for salt content, i.e., for desorbing area,
Coefficient a is conspicuousness related coefficient (P < 0.01), according to the significance test result and R of coefficient a2Value, RH=0.85~0.87
The sub- area of desorption influenced significant and the section R by salt content2Value is maximum, therefore can determine that the section is desorption curve
Sensitivity interval.And in severe salinized soil, exist between salt content and moisture absorption area, desorption area and lag area significant
Correlation, i.e. correlation test result P < 0.01 of coefficient a, wherein sucting wet curve sensibility section is RH=0.91~0.93,
Desorption curve sensibility section is RH=0.79~0.81, and hysteresis sensibility section is 0.61~0.63.
S6: after finding the sensitivity interval significantly affected by soil salt content or clay content according to step S5, soil is established
Best dependency prediction model formation between geometrical characteristic area in earth clay content or salt content and sensitivity interval, and
Determine the scope of application, specific prediction model formula is shown in Table 4；Meanwhile filtering out coefficient of determination R2Maximum sensibility section
Prediction model formula is for predicting soil clay particle content or salt content.
Table 4 is the prediction model formula that geometric area predicts soil clay particle content or salt content in sensibility section
As shown in table 4, for non-salty-soil, clay content is the principal element for influencing soil water regime, RH=0.85
~0.87 sub- area of desorption and the sub- area of lag of RH=0.79~0.81 can be predicted to glue by simple linearity regress equation
Grain content, the good (R of correlation2>0.9；RRMSE < 10%；RMAE < 10%), the wherein sub- area of the desorption of RH=0.85~0.87
For predicting that clay content effect is best.And for slight salinized soil, the sub- Area Prediction clay of the lag of RH=0.81~0.83
Content effect is optimal.In moderate salinized soil and severe salinized soil, salt content significantly affects sucting wet curve and desorption curve
Variation can predict soil salt content by equation of linear regression using the sub- area of moisture absorption and the sub- area of desorption.Moderate salinized soil
In, the prediction effect of the sub- area of desorption of RH=0.87~0.89 is better than the sub- area (RH=of moisture absorption of RH=0.15~0.17
The R of 0.87~0.89 sub- area of desorption2Value is greater than the R of the sub- area of moisture absorption of RH=0.15~0.172Value；And RH=0.87~
The RRMSE and RMAE of the 0.89 sub- area of desorption are less than the RRMSE and RMAE of the sub- area of moisture absorption of RH=0.15~0.17).And
In severe salinized soil, the sub- Area Prediction effect of the moisture absorption of RH=0.91~0.93 is better than the sub- face of desorption of RH=0.79~0.81
Product (the R of the sub- area of the moisture absorption of RH=0.91~0.932Value is greater than the R of the sub- area of desorption of RH=0.79~0.812Value, and RH=
The RRMSE and RMAE of the sub- area of 0.91~0.93 moisture absorption be less than the sub- area of desorption of RH=0.79~0.81 RRMSE and
RMAE)。
When considering entire soil sample, Adsorption and desorption and the sub- area of lag are gradually increased with the increase of soil salt,
Its scatter plot is parabolic type.Therefore, binomial regression equation can preferably be fitted the relationship (R of sub- area and salt content2=
0.804~0.928), but error increases (RRMSE > 15%；RMAE > 10%).Wherein, moisture absorption of RH=0.83~0.85
Area Prediction salt content effect is best, but its error is still bigger than moderate salinized soil and severe salinized soil.
Therefore when soil sample salt content is smaller, the desorption area or lag Area Prediction soil clay particle in sensitivity interval can be used
Content；When soil sample salt content range is larger, the moisture absorption in sensibility section or desorption Area Prediction salt content can be used, sentence
Disconnected soil salt is horizontal.
For the reliability for the prediction model formula established in check table 4,5 soil samples is selected to separately verify prediction mould
Type formula (6)~(10) reliability, including 1 non-salty-soil earth, 1 slight salinized soil, 1 moderate salt-affected soil and 2
Severe salt-affected soil, reliability is using error amount as index.Clay error amount calculation formula is Erclay=| CLp-CLi|, salinity misses
Difference calculation formula is
Table 5 is the index for verifying prediction model formula reliability
As shown in table 5, non-salty-soil and light salinized soil pass through the clay content and actual measurement that empirical equation (6) and (7) obtain
Error amount is smaller (< 1%) between clay content；What moderate salinized soil and severe salinized soil were obtained by empirical equation (8) and (9)
For error less than 5%, prediction effect is good between salt content and actual measurement salt content.5 soil are obtained by empirical equation (10)
Mean error between the salt content arrived and actual measurement salt content is 26.2%, wherein the salinity of non-salt soil and light salt soil
Error amount is much larger than middle salt and geavy salt soil, but predicted value is still within corresponding salinity level.Therefore, for salinity level model
Biggish soil sample is enclosed, it is good using formula (10) prediction effect when salt content is higher, but when salt content is lower, together
Sample may determine that salinity level locating for less salt soil.Therefore, the prediction model formula established in table 4 is more reliable.
When being predicted for great amount of samples, first a large amount of soil samples can be calculated by formula (10) each
The rough salt content of soil sample judges that it is specifically in which of four kinds of salinity levels further according to obtained substantially salt content
In a salinity level, then according to the i.e. corresponding prediction model formula of the applicable optimum prediction model formation of salinity level where it
(6) one of~(9) carry out prediction soil clay particle content or salt content.
The above is a preferred embodiment of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, it is noted that for those skilled in the art, without departing from the principle of the present invention, may be used also
To make several improvement and variation, these, which improve and change, is also considered as protection scope of the present invention.
Claims (10)
1. a kind of method for predicting soil clay particle content or salt content based on adsorption isothermal curve, which is characterized in that including such as
Lower step:
S1: the selected part soil sample in large sample is contained with the clay that traditional measurement method measures the pedotheque of selection
Amount and salt content；
S2: the vapor adsorption isothermal curve eyeball of the soil sample of selection is measured, then is completely adsorbed by fitting
Curve and desorption curve；
S3: the geometrical characteristic area of the soil water vapour adsorption isothermal curve of the soil sample of selection, including moisture absorption, desorption are calculated
With the geometrical characteristic area of lag；
S4: using Multiple Regression Analysis Method to the geometrical characteristic face of soil salt content, clay content and adsorption isothermal curve
Product carries out significant correlation analysis, determines that geometrical characteristic area is influenced significant sensitizing range by soil clay particle content and salt content
Between；
S5: it establishes soil clay particle content, the prediction model formula in salt content and sensitivity interval between geometric area, and determines
The scope of application；
S6: the soil sample for needing to measure clay content or salt content is calculated by above-mentioned corresponding prediction model formula
To its clay content or salt content.
2. the method according to claim 1 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
Be characterized in that, the soil sample of selection from the soil of 4 kinds of salinity levels, be respectively soil saturation leaching liquor conductivity be 0~
The non-salty-soil of 4.5dS/m, slight salinized soil, the soil saturation leaching liquor that soil saturation leaching liquor conductivity is 4.5~9dS/m
Moderate salinized soil and soil saturation leaching liquor conductivity that conductivity is 9~18dS/m are greater than the severe salinized soil of 18dS/m,
Wherein the soil sample of every kind of salinity level all has multiple.
3. the method according to claim 1 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, the geometrical characteristic area of soil salt content, clay content and adsorption isothermal curve is shown in step s 4
Correlation analysis when, the geometrical characteristic area for choosing the moisture absorption under a variety of RH step-lengths, desorption and lag is calculated and is analyzed.
4. the method according to claim 2 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, the geometrical characteristic area of soil salt content, clay content and adsorption isothermal curve is shown in step s 4
Correlation analysis when, the geometrical characteristic area for choosing the moisture absorption under a variety of RH step-lengths, desorption and lag is calculated and is analyzed.
5. the method according to claim 3 or 4 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, the step-length of RH is chosen for 0.02,0.1 and 0.9 respectively.
6. the method according to claim 3 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, step S4 further include:
When the geometrical characteristic area to soil salt, clay content and adsorption isothermal curve carries out significant correlation analysis, build
The mathematic(al) representation of vertical triadic relation:
A=aEc+bCL+c
Wherein, ECFor conductivity, dS/m；CL is clay content, %；A is the moisture absorption of soil water vapour adsorption isothermal curve, desorption
With the geometrical characteristic area of lag, the sub- area including the gross area and a variety of RH step-lengths section；A, b and c is regression coefficient；
By fit procedure S2 in the clay content of the soil sample of all selections, salt content obtained in step S1 and step S3
The adsorption curve and desorption curve that the vapor adsorption isothermal curve eyeball of middle soil sample obtains are walked in corresponding a variety of RH
Moisture absorption area, desorption area and the lag area that long interval computation obtains are substituted into respectively in above-mentioned mathematic(al) representation, are calculated separately
All soil samples chosen are between the numerical value, fitting area and eyeball area of coefficient a, b, c in each RH step-length section
Coefficient of determination R2, and test to the conspicuousness of coefficient a, b；
According to the coefficient of determination R between the significance test result of coefficient a, b, matched curve area and eyeball area2Value determines
The sensitivity interval that the geometrical characteristic area of the soil sample of all selections is significantly affected by soil clay particle content or salt content out,
The significant correlation and coefficient of determination R of coefficient a, the b of the soil sample of i.e. more all selections in a variety of RH step-lengths section2
Value, wherein a is that significant related coefficient is then influenced significantly by salt content, and b is that significant related coefficient then is influenced to show by clay content
It writes, and the coefficient of determination R between matched curve area and eyeball area2The maximum corresponding section of value is the soil of all selections
The sensitivity interval of earth sample.
7. the method according to claim 6 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, establishes the prediction model formula for being suitable for all soil, prediction model formula are as follows:
The sensitivity interval of all soil is RH=0.83~0.85, the prediction model formula of salt content are as follows: Ec(p)=-4.66 ×
107SAa 2+1.38×105SAa- 44.18, wherein Ec(p)For the conductivity of soil prediction, SAaFor the sucting wet curve face of sensitivity interval
Product.
8. the method according to claim 4 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, step S4 further include:
When the geometrical characteristic area to soil salt, clay content and adsorption isothermal curve carries out significant correlation analysis, build
The mathematic(al) representation of vertical triadic relation:
A=aEc+bCL+c
Wherein, ECFor conductivity, dS/m；CL is clay content, %；A is the moisture absorption of soil water vapour adsorption isothermal curve, desorption
With the geometrical characteristic area of lag, the sub- area including the gross area and a variety of RH step-lengths section；A, b and c is regression coefficient；
By fit procedure S2 in the clay content of the soil sample of all selections, salt content obtained in step S1 and step S3
The adsorption curve and desorption curve that the vapor adsorption isothermal curve eyeball of middle soil sample obtains are walked in corresponding a variety of RH
Moisture absorption area, desorption area and the lag area that long interval computation obtains are substituted into respectively in above-mentioned mathematic(al) representation, are calculated separately
All soil samples chosen are between the numerical value, fitting area and eyeball area of coefficient a, b, c in each RH step-length section
Coefficient of determination R2, and test to the conspicuousness of coefficient a, b；It is bent according to the significance test result of coefficient a, b, fitting
Coefficient of determination R between line area and eyeball area2Value determine the geometrical characteristic area of the soil sample of all selections by
The sensitivity interval that soil clay particle content or salt content significantly affect, i.e., the soil sample of more all selections is in a variety of RH step-lengths
The significant correlation and coefficient of determination R of coefficient a, b in section2Value, wherein a be significant related coefficient then its contained by salinity
Amount influence it is significant, b be significant related coefficient then its influenced significantly by clay content, and be fitted between area and eyeball area
Coefficient of determination R2The maximum corresponding section of value is the sensitivity interval of the soil sample of all selections；
Further according to the classification for four kinds of salinity levels that the above-mentioned soil sample to selection carries out, every kind of salinity level is individually calculated
Soil sample in the numerical value of coefficient a, b, c in each RH step-length section, the significant correlation of coefficient a, b and fitting area and reality
Coefficient of determination R between measuring point area2Value；According between the significant correlation of coefficient a, b, fitting area and eyeball area
Coefficient of determination R2Determine the geometrical characteristic area of the soil sample of four kinds of salinity levels by soil clay particle content or salt content
The sensitivity interval significantly affected, wherein for the soil sample of every kind of salinity level, a be significant related coefficient then its by salinity
Content influence it is significant, b be significant related coefficient then its influenced significantly by clay content, and be fitted between area and eyeball area
Coefficient of determination R2The maximum corresponding section of value is its sensitivity interval.
9. the method according to claim 8 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, the soil for establishing all soil and four kinds of salinity levels predicts soil with geometric area in its sensibility section respectively
The model formation of earth clay content or salt content, prediction model formula are respectively as follows:
The sensitivity interval of all soil is RH=0.83~0.85, the prediction model formula of salt content are as follows: Ec(p)=-4.66 ×
107SAa 2+1.38×105SAa- 44.18, wherein Ec(p)Conductivity, SA are predicted for soilaFor the sucting wet curve face of sensitivity interval
Product；
The sensitivity interval of non-salty-soil is RH=0.85~0.87, clay content prediction model formula are as follows: CLp=4.34 ×
105SAd- 188.83, wherein for CLpTo predict clay content, SAdFor the desorption curve area of sensitivity interval；
The sensitivity interval of slight salinized soil is RH=0.81~0.83, clay content prediction model formula are as follows: CLp=-3.09 ×
105SAh+ 43.97, wherein for CLpTo predict clay content, SAhFor the lag area of sensitivity interval；
The sensitivity interval of moderate salinized soil is RH=0.87~0.89, salt content prediction model formula are as follows: Ec(p)=9.38 ×
104SAd- 41.82, wherein Ec(p)To predict conductivity content, SAdFor the desorption curve area of sensitivity interval；
The sensitivity interval of severe salinized soil is RH=0.91~0.93, salt content prediction model formula are as follows: Ec(p)=2.21 ×
104SAa+ 4.32, wherein Ec(p)To predict conductivity content, SAaFor the sucting wet curve area of sensitivity interval.
10. the method according to claim 9 that soil clay particle content or salt content are predicted based on adsorption isothermal curve,
It is characterized in that, when predicting great amount of samples, first fits each soil sample in a large amount of soil samples by above-mentioned
The substantially salt content of each soil sample, then root are calculated for the salt content prediction model formula of all soil samples
Judge it particularly belongs to which kind of salinity level in four kinds of salinity levels according to the substantially salt content that each soil sample is predicted
Soil finally carries out prediction soil clay particle content according to the prediction model formula that its corresponding salinity level is applicable in or salinity contains
Amount.
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